Overview

Dataset statistics

Number of variables21
Number of observations8920
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory164.0 B

Variable types

Numeric21

Alerts

df_index is highly correlated with cust_idHigh correlation
cust_id is highly correlated with df_indexHigh correlation
balance is highly correlated with balance_frequency and 5 other fieldsHigh correlation
balance_frequency is highly correlated with balance and 1 other fieldsHigh correlation
purchases is highly correlated with oneoff_purchases and 6 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance is highly correlated with balance and 3 other fieldsHigh correlation
purchases_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance_frequency is highly correlated with balance and 3 other fieldsHigh correlation
cash_advance_trx is highly correlated with balance and 3 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 5 other fieldsHigh correlation
minimum_payments is highly correlated with balance and 1 other fieldsHigh correlation
purchases_avg is highly correlated with purchases and 2 other fieldsHigh correlation
cash_advance_avg is highly correlated with balance and 3 other fieldsHigh correlation
df_index is highly correlated with cust_idHigh correlation
cust_id is highly correlated with df_indexHigh correlation
balance is highly correlated with credit_limitHigh correlation
purchases is highly correlated with oneoff_purchases and 3 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_frequency and 1 other fieldsHigh correlation
purchases_frequency is highly correlated with oneoff_purchases_frequency and 2 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with oneoff_purchases and 2 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with installments_purchases and 2 other fieldsHigh correlation
cash_advance_frequency is highly correlated with cash_advance and 1 other fieldsHigh correlation
cash_advance_trx is highly correlated with cash_advance and 1 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 5 other fieldsHigh correlation
credit_limit is highly correlated with balanceHigh correlation
payments is highly correlated with purchases and 1 other fieldsHigh correlation
df_index is highly correlated with cust_idHigh correlation
cust_id is highly correlated with df_indexHigh correlation
balance is highly correlated with minimum_paymentsHigh correlation
purchases is highly correlated with oneoff_purchases and 5 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_frequency and 2 other fieldsHigh correlation
purchases_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases and 1 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with installments_purchases and 2 other fieldsHigh correlation
cash_advance_frequency is highly correlated with cash_advance and 2 other fieldsHigh correlation
cash_advance_trx is highly correlated with cash_advance and 2 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 3 other fieldsHigh correlation
minimum_payments is highly correlated with balanceHigh correlation
purchases_avg is highly correlated with purchases and 1 other fieldsHigh correlation
cash_advance_avg is highly correlated with cash_advance and 2 other fieldsHigh correlation
df_index is highly correlated with cust_idHigh correlation
cust_id is highly correlated with df_indexHigh correlation
balance is highly correlated with credit_limitHigh correlation
purchases is highly correlated with oneoff_purchases and 4 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 1 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_trx and 1 other fieldsHigh correlation
purchases_frequency is highly correlated with oneoff_purchases_frequency and 1 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases_frequencyHigh correlation
purchases_installments_frequency is highly correlated with purchases_frequencyHigh correlation
cash_advance_frequency is highly correlated with cash_advance_trxHigh correlation
cash_advance_trx is highly correlated with cash_advance and 1 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 3 other fieldsHigh correlation
credit_limit is highly correlated with balance and 2 other fieldsHigh correlation
payments is highly correlated with purchases and 5 other fieldsHigh correlation
purchases_avg is highly correlated with paymentsHigh correlation
df_index is uniformly distributed Uniform
cust_id is uniformly distributed Uniform
df_index has unique values Unique
cust_id has unique values Unique
purchases has 2038 (22.8%) zeros Zeros
oneoff_purchases has 4283 (48.0%) zeros Zeros
installments_purchases has 3904 (43.8%) zeros Zeros
cash_advance has 4612 (51.7%) zeros Zeros
purchases_frequency has 2038 (22.8%) zeros Zeros
oneoff_purchases_frequency has 4283 (48.0%) zeros Zeros
purchases_installments_frequency has 3904 (43.8%) zeros Zeros
cash_advance_frequency has 4612 (51.7%) zeros Zeros
cash_advance_trx has 4612 (51.7%) zeros Zeros
purchases_trx has 2036 (22.8%) zeros Zeros
payments has 240 (2.7%) zeros Zeros
minimum_payments has 313 (3.5%) zeros Zeros
prc_full_payment has 5878 (65.9%) zeros Zeros
purchases_avg has 2038 (22.8%) zeros Zeros
cash_advance_avg has 4612 (51.7%) zeros Zeros

Reproduction

Analysis started2021-11-30 12:49:31.838214
Analysis finished2021-11-30 12:51:16.243143
Duration1 minute and 44.4 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct8920
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4474.136883
Minimum0
Maximum8949
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:16.548333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile445.95
Q12236.75
median4472.5
Q36712.25
95-th percentile8502.05
Maximum8949
Range8949
Interquartile range (IQR)4475.5

Descriptive statistics

Standard deviation2584.092911
Coefficient of variation (CV)0.5775623272
Kurtosis-1.200105095
Mean4474.136883
Median Absolute Deviation (MAD)2237.5
Skewness0.0003342727901
Sum39909301
Variance6677536.171
MonotonicityStrictly increasing
2021-11-30T09:51:16.722915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
6051
 
< 0.1%
26441
 
< 0.1%
5971
 
< 0.1%
67421
 
< 0.1%
46951
 
< 0.1%
87931
 
< 0.1%
26521
 
< 0.1%
67501
 
< 0.1%
26681
 
< 0.1%
Other values (8910)8910
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
89491
< 0.1%
89481
< 0.1%
89471
< 0.1%
89461
< 0.1%
89451
< 0.1%
89441
< 0.1%
89431
< 0.1%
89421
< 0.1%
89411
< 0.1%
89401
< 0.1%

cust_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct8920
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14599.66637
Minimum10001
Maximum19190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2021-11-30T09:51:16.912623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10461.95
Q112306.75
median14596.5
Q316900.25
95-th percentile18733.05
Maximum19190
Range9189
Interquartile range (IQR)4593.5

Descriptive statistics

Standard deviation2651.620337
Coefficient of variation (CV)0.1816219816
Kurtosis-1.199360604
Mean14599.66637
Median Absolute Deviation (MAD)2296.5
Skewness-0.0004945342566
Sum130229024
Variance7031090.412
MonotonicityStrictly increasing
2021-11-30T09:51:17.087339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163841
 
< 0.1%
149221
 
< 0.1%
108161
 
< 0.1%
149141
 
< 0.1%
128671
 
< 0.1%
190121
 
< 0.1%
169651
 
< 0.1%
108241
 
< 0.1%
128751
 
< 0.1%
108401
 
< 0.1%
Other values (8910)8910
99.9%
ValueCountFrequency (%)
100011
< 0.1%
100021
< 0.1%
100031
< 0.1%
100041
< 0.1%
100051
< 0.1%
100061
< 0.1%
100071
< 0.1%
100081
< 0.1%
100091
< 0.1%
100101
< 0.1%
ValueCountFrequency (%)
191901
< 0.1%
191891
< 0.1%
191881
< 0.1%
191871
< 0.1%
191861
< 0.1%
191851
< 0.1%
191841
< 0.1%
191831
< 0.1%
191821
< 0.1%
191811
< 0.1%

balance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8841
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1562.512922
Minimum0
Maximum19043.13856
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:17.267672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.82004985
Q1128.3728552
median871.534477
Q32049.707459
95-th percentile5906.232841
Maximum19043.13856
Range19043.13856
Interquartile range (IQR)1921.334604

Descriptive statistics

Standard deviation2079.408643
Coefficient of variation (CV)1.330810525
Kurtosis7.714122828
Mean1562.512922
Median Absolute Deviation (MAD)798.3892365
Skewness2.397770669
Sum13937615.27
Variance4323940.304
MonotonicityNot monotonic
2021-11-30T09:51:17.435344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
080
 
0.9%
74.8254561
 
< 0.1%
155.6762551
 
< 0.1%
34.7507691
 
< 0.1%
2643.3434141
 
< 0.1%
6366.0853761
 
< 0.1%
1112.9156571
 
< 0.1%
2545.6149861
 
< 0.1%
449.0268891
 
< 0.1%
1722.9100581
 
< 0.1%
Other values (8831)8831
99.0%
ValueCountFrequency (%)
080
0.9%
0.0001991
 
< 0.1%
0.0011461
 
< 0.1%
0.0012141
 
< 0.1%
0.0012891
 
< 0.1%
0.0048161
 
< 0.1%
0.0066511
 
< 0.1%
0.0096841
 
< 0.1%
0.019681
 
< 0.1%
0.0211021
 
< 0.1%
ValueCountFrequency (%)
19043.138561
< 0.1%
18495.558551
< 0.1%
16304.889251
< 0.1%
16259.448571
< 0.1%
16115.59641
< 0.1%
15532.339721
< 0.1%
15258.22591
< 0.1%
15244.748651
< 0.1%
15155.532861
< 0.1%
14581.459141
< 0.1%

balance_frequency
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8772435415
Minimum0
Maximum1
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:17.618280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.272727
Q10.888889
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.111111

Descriptive statistics

Standard deviation0.2369232478
Coefficient of variation (CV)0.270076936
Kurtosis3.096858681
Mean0.8772435415
Median Absolute Deviation (MAD)0
Skewness-2.023939108
Sum7825.01239
Variance0.05613262536
MonotonicityNot monotonic
2021-11-30T09:51:17.792818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
16189
69.4%
0.909091408
 
4.6%
0.818182278
 
3.1%
0.727273223
 
2.5%
0.545455219
 
2.5%
0.636364209
 
2.3%
0.363636170
 
1.9%
0.454545169
 
1.9%
0.272727149
 
1.7%
0.181818146
 
1.6%
Other values (33)760
 
8.5%
ValueCountFrequency (%)
080
0.9%
0.09090967
0.8%
0.18
 
0.1%
0.1111115
 
0.1%
0.1259
 
0.1%
0.1428577
 
0.1%
0.1666677
 
0.1%
0.181818146
1.6%
0.29
 
0.1%
0.2222225
 
0.1%
ValueCountFrequency (%)
16189
69.4%
0.909091408
 
4.6%
0.955
 
0.6%
0.88888953
 
0.6%
0.87557
 
0.6%
0.85714351
 
0.6%
0.83333359
 
0.7%
0.818182278
 
3.1%
0.820
 
0.2%
0.77777822
 
0.2%

purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6180
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1004.733073
Minimum0
Maximum49039.57
Zeros2038
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:17.968544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.57
median362.305
Q31112.425
95-th percentile4000.009
Maximum49039.57
Range49039.57
Interquartile range (IQR)1072.855

Descriptive statistics

Standard deviation2139.075941
Coefficient of variation (CV)2.128999233
Kurtosis111.2247759
Mean1004.733073
Median Absolute Deviation (MAD)362.305
Skewness8.140453832
Sum8962219.01
Variance4575645.883
MonotonicityNot monotonic
2021-11-30T09:51:18.151121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02038
 
22.8%
45.6527
 
0.3%
6016
 
0.2%
15016
 
0.2%
30013
 
0.1%
10013
 
0.1%
20013
 
0.1%
45012
 
0.1%
7010
 
0.1%
12010
 
0.1%
Other values (6170)6752
75.7%
ValueCountFrequency (%)
02038
22.8%
0.014
 
< 0.1%
0.051
 
< 0.1%
0.71
 
< 0.1%
12
 
< 0.1%
1.41
 
< 0.1%
21
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
4.991
 
< 0.1%
ValueCountFrequency (%)
49039.571
< 0.1%
41050.41
< 0.1%
40040.711
< 0.1%
38902.711
< 0.1%
35131.161
< 0.1%
32539.781
< 0.1%
31299.351
< 0.1%
27957.681
< 0.1%
27790.421
< 0.1%
26784.621
< 0.1%

oneoff_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4004
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean593.7731491
Minimum0
Maximum40761.25
Zeros4283
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:18.333571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median39
Q3581.015
95-th percentile2675.0315
Maximum40761.25
Range40761.25
Interquartile range (IQR)581.015

Descriptive statistics

Standard deviation1662.139174
Coefficient of variation (CV)2.799283154
Kurtosis163.8101308
Mean593.7731491
Median Absolute Deviation (MAD)39
Skewness10.0351636
Sum5296456.49
Variance2762706.633
MonotonicityNot monotonic
2021-11-30T09:51:18.514595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04283
48.0%
45.6545
 
0.5%
5017
 
0.2%
20015
 
0.2%
6013
 
0.1%
10013
 
0.1%
100012
 
0.1%
7012
 
0.1%
15012
 
0.1%
25011
 
0.1%
Other values (3994)4487
50.3%
ValueCountFrequency (%)
04283
48.0%
0.017
 
0.1%
0.022
 
< 0.1%
0.051
 
< 0.1%
0.71
 
< 0.1%
14
 
< 0.1%
1.42
 
< 0.1%
21
 
< 0.1%
4.991
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
40761.251
< 0.1%
40624.061
< 0.1%
34087.731
< 0.1%
33803.841
< 0.1%
26547.431
< 0.1%
26514.321
< 0.1%
25122.771
< 0.1%
24543.521
< 0.1%
23032.971
< 0.1%
22257.391
< 0.1%

installments_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4437
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410.9599238
Minimum0
Maximum22500
Zeros3904
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:18.715807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89.18
Q3467.34
95-th percentile1753.284
Maximum22500
Range22500
Interquartile range (IQR)467.34

Descriptive statistics

Standard deviation903.5509971
Coefficient of variation (CV)2.198635305
Kurtosis97.06942256
Mean410.9599238
Median Absolute Deviation (MAD)89.18
Skewness7.313374299
Sum3665762.52
Variance816404.4044
MonotonicityNot monotonic
2021-11-30T09:51:18.885551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03904
43.8%
20014
 
0.2%
10014
 
0.2%
30014
 
0.2%
15012
 
0.1%
12511
 
0.1%
759
 
0.1%
4508
 
0.1%
3508
 
0.1%
5008
 
0.1%
Other values (4427)4918
55.1%
ValueCountFrequency (%)
03904
43.8%
1.951
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
6.331
 
< 0.1%
7.261
 
< 0.1%
7.671
 
< 0.1%
9.281
 
< 0.1%
9.581
 
< 0.1%
9.651
 
< 0.1%
ValueCountFrequency (%)
225001
< 0.1%
15497.191
< 0.1%
14686.11
< 0.1%
13184.431
< 0.1%
12738.471
< 0.1%
12560.851
< 0.1%
125411
< 0.1%
123751
< 0.1%
12235.051
< 0.1%
12128.941
< 0.1%

cash_advance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4309
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean977.9975392
Minimum0
Maximum47137.21176
Zeros4612
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:19.055654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31113.273292
95-th percentile4639.920152
Maximum47137.21176
Range47137.21176
Interquartile range (IQR)1113.273292

Descriptive statistics

Standard deviation2097.519305
Coefficient of variation (CV)2.144708162
Kurtosis53.0434044
Mean977.9975392
Median Absolute Deviation (MAD)0
Skewness5.176949006
Sum8723738.05
Variance4399587.234
MonotonicityNot monotonic
2021-11-30T09:51:19.224852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04612
51.7%
920.3098051
 
< 0.1%
724.6944111
 
< 0.1%
1420.7582411
 
< 0.1%
2081.5833311
 
< 0.1%
2970.979361
 
< 0.1%
4353.6200391
 
< 0.1%
8641.9989581
 
< 0.1%
4962.9368391
 
< 0.1%
3061.0148371
 
< 0.1%
Other values (4299)4299
48.2%
ValueCountFrequency (%)
04612
51.7%
14.2222161
 
< 0.1%
18.0427681
 
< 0.1%
18.1179671
 
< 0.1%
18.1234131
 
< 0.1%
18.1266831
 
< 0.1%
18.1499461
 
< 0.1%
18.2045771
 
< 0.1%
18.2406261
 
< 0.1%
18.2800431
 
< 0.1%
ValueCountFrequency (%)
47137.211761
< 0.1%
29282.109151
< 0.1%
27296.485761
< 0.1%
26268.699891
< 0.1%
26194.049541
< 0.1%
23130.821061
< 0.1%
22665.77851
< 0.1%
21943.849421
< 0.1%
20712.670081
< 0.1%
20277.331121
< 0.1%

purchases_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4905560407
Minimum0
Maximum1
Zeros2038
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:19.404398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.4014905514
Coefficient of variation (CV)0.8184397256
Kurtosis-1.63936366
Mean0.4905560407
Median Absolute Deviation (MAD)0.416667
Skewness0.05888288058
Sum4375.759883
Variance0.1611946628
MonotonicityNot monotonic
2021-11-30T09:51:19.577541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
12173
24.4%
02038
22.8%
0.083333675
 
7.6%
0.5394
 
4.4%
0.916667394
 
4.4%
0.166667391
 
4.4%
0.833333372
 
4.2%
0.333333365
 
4.1%
0.25342
 
3.8%
0.583333315
 
3.5%
Other values (37)1461
16.4%
ValueCountFrequency (%)
02038
22.8%
0.083333675
 
7.6%
0.09090943
 
0.5%
0.126
 
0.3%
0.11111118
 
0.2%
0.12531
 
0.3%
0.14285726
 
0.3%
0.166667391
 
4.4%
0.18181816
 
0.2%
0.219
 
0.2%
ValueCountFrequency (%)
12173
24.4%
0.916667394
 
4.4%
0.90909128
 
0.3%
0.924
 
0.3%
0.88888918
 
0.2%
0.87526
 
0.3%
0.85714325
 
0.3%
0.833333372
 
4.2%
0.81818221
 
0.2%
0.89
 
0.1%

oneoff_purchases_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.202867074
Minimum0
Maximum1
Zeros4283
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:19.755090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.3
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2986539051
Coefficient of variation (CV)1.472165489
Kurtosis1.149099359
Mean0.202867074
Median Absolute Deviation (MAD)0.083333
Skewness1.532304114
Sum1809.5743
Variance0.08919415504
MonotonicityNot monotonic
2021-11-30T09:51:19.937799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
04283
48.0%
0.0833331101
 
12.3%
0.166667592
 
6.6%
1481
 
5.4%
0.25415
 
4.7%
0.333333354
 
4.0%
0.416667244
 
2.7%
0.5234
 
2.6%
0.583333197
 
2.2%
0.666667167
 
1.9%
Other values (37)852
 
9.6%
ValueCountFrequency (%)
04283
48.0%
0.0833331101
 
12.3%
0.09090956
 
0.6%
0.138
 
0.4%
0.11111126
 
0.3%
0.12540
 
0.4%
0.14285737
 
0.4%
0.166667592
 
6.6%
0.18181834
 
0.4%
0.227
 
0.3%
ValueCountFrequency (%)
1481
5.4%
0.916667151
 
1.7%
0.9090914
 
< 0.1%
0.91
 
< 0.1%
0.8888892
 
< 0.1%
0.8756
 
0.1%
0.8571431
 
< 0.1%
0.833333120
 
1.3%
0.81818210
 
0.1%
0.84
 
< 0.1%

purchases_installments_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3644958629
Minimum0
Maximum1
Zeros3904
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:20.126297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.3974575441
Coefficient of variation (CV)1.090430879
Kurtosis-1.3991404
Mean0.3644958629
Median Absolute Deviation (MAD)0.166667
Skewness0.5086379574
Sum3251.303097
Variance0.1579724994
MonotonicityNot monotonic
2021-11-30T09:51:20.308408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
03904
43.8%
11326
 
14.9%
0.416667388
 
4.3%
0.916667344
 
3.9%
0.833333310
 
3.5%
0.5309
 
3.5%
0.166667303
 
3.4%
0.666667291
 
3.3%
0.75290
 
3.3%
0.083333271
 
3.0%
Other values (37)1184
 
13.3%
ValueCountFrequency (%)
03904
43.8%
0.083333271
 
3.0%
0.09090912
 
0.1%
0.16
 
0.1%
0.1111119
 
0.1%
0.1255
 
0.1%
0.1428576
 
0.1%
0.166667303
 
3.4%
0.18181814
 
0.2%
0.29
 
0.1%
ValueCountFrequency (%)
11326
14.9%
0.916667344
 
3.9%
0.90909125
 
0.3%
0.919
 
0.2%
0.88888928
 
0.3%
0.87528
 
0.3%
0.85714330
 
0.3%
0.833333310
 
3.5%
0.81818221
 
0.2%
0.818
 
0.2%

cash_advance_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1343701601
Minimum0
Maximum1
Zeros4612
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:20.495287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.222222
95-th percentile0.583333
Maximum1
Range1
Interquartile range (IQR)0.222222

Descriptive statistics

Standard deviation0.1977454896
Coefficient of variation (CV)1.471647347
Kurtosis2.886218301
Mean0.1343701601
Median Absolute Deviation (MAD)0
Skewness1.769252535
Sum1198.581828
Variance0.03910327864
MonotonicityNot monotonic
2021-11-30T09:51:20.667905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
04612
51.7%
0.0833331018
 
11.4%
0.166667759
 
8.5%
0.25577
 
6.5%
0.333333438
 
4.9%
0.416667273
 
3.1%
0.5215
 
2.4%
0.583333141
 
1.6%
0.666667125
 
1.4%
0.09090970
 
0.8%
Other values (37)692
 
7.8%
ValueCountFrequency (%)
04612
51.7%
0.0833331018
 
11.4%
0.09090970
 
0.8%
0.139
 
0.4%
0.11111129
 
0.3%
0.12547
 
0.5%
0.14285749
 
0.5%
0.166667759
 
8.5%
0.18181842
 
0.5%
0.221
 
0.2%
ValueCountFrequency (%)
125
0.3%
0.91666727
0.3%
0.9090913
 
< 0.1%
0.92
 
< 0.1%
0.8888892
 
< 0.1%
0.8755
 
0.1%
0.8571435
 
0.1%
0.83333348
0.5%
0.8181822
 
< 0.1%
0.86
 
0.1%

cash_advance_trx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.239461883
Minimum0
Maximum123
Zeros4612
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:20.838593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.814954483
Coefficient of variation (CV)2.103730412
Kurtosis62.21440988
Mean3.239461883
Median Absolute Deviation (MAD)0
Skewness5.752721811
Sum28896
Variance46.4436046
MonotonicityNot monotonic
2021-11-30T09:51:21.017624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04612
51.7%
1886
 
9.9%
2619
 
6.9%
3436
 
4.9%
4382
 
4.3%
5307
 
3.4%
6246
 
2.8%
7205
 
2.3%
8171
 
1.9%
10149
 
1.7%
Other values (55)907
 
10.2%
ValueCountFrequency (%)
04612
51.7%
1886
 
9.9%
2619
 
6.9%
3436
 
4.9%
4382
 
4.3%
5307
 
3.4%
6246
 
2.8%
7205
 
2.3%
8171
 
1.9%
9111
 
1.2%
ValueCountFrequency (%)
1233
< 0.1%
1101
 
< 0.1%
1071
 
< 0.1%
931
 
< 0.1%
801
 
< 0.1%
711
 
< 0.1%
691
 
< 0.1%
631
 
< 0.1%
623
< 0.1%
611
 
< 0.1%

purchases_trx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct173
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.72466368
Minimum0
Maximum358
Zeros2036
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:21.195758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q317
95-th percentile57
Maximum358
Range358
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.87834234
Coefficient of variation (CV)1.689569479
Kurtosis34.77673531
Mean14.72466368
Median Absolute Deviation (MAD)7
Skewness4.630671453
Sum131344
Variance618.9319177
MonotonicityNot monotonic
2021-11-30T09:51:21.358963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02036
22.8%
1665
 
7.5%
12570
 
6.4%
2376
 
4.2%
6350
 
3.9%
3313
 
3.5%
4285
 
3.2%
7274
 
3.1%
8267
 
3.0%
5263
 
2.9%
Other values (163)3521
39.5%
ValueCountFrequency (%)
02036
22.8%
1665
 
7.5%
2376
 
4.2%
3313
 
3.5%
4285
 
3.2%
5263
 
2.9%
6350
 
3.9%
7274
 
3.1%
8267
 
3.0%
9248
 
2.8%
ValueCountFrequency (%)
3581
< 0.1%
3471
< 0.1%
3441
< 0.1%
3091
< 0.1%
3081
< 0.1%
2981
< 0.1%
2741
< 0.1%
2731
< 0.1%
2541
< 0.1%
2482
< 0.1%

credit_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct205
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4492.51997
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:21.537701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range29950
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3638.641132
Coefficient of variation (CV)0.8099332126
Kurtosis2.845871581
Mean4492.51997
Median Absolute Deviation (MAD)1800
Skewness1.523857227
Sum40073278.13
Variance13239709.29
MonotonicityNot monotonic
2021-11-30T09:51:21.706080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000781
 
8.8%
1500721
 
8.1%
1200619
 
6.9%
2500611
 
6.8%
1000610
 
6.8%
4000504
 
5.7%
6000461
 
5.2%
5000387
 
4.3%
2000370
 
4.1%
7500277
 
3.1%
Other values (195)3579
40.1%
ValueCountFrequency (%)
502
 
< 0.1%
1505
 
0.1%
2003
 
< 0.1%
30014
 
0.2%
4003
 
< 0.1%
4506
 
0.1%
500121
1.4%
60021
 
0.2%
6501
 
< 0.1%
70020
 
0.2%
ValueCountFrequency (%)
300002
 
< 0.1%
280001
 
< 0.1%
250001
 
< 0.1%
230002
 
< 0.1%
225001
 
< 0.1%
220001
 
< 0.1%
215002
 
< 0.1%
210002
 
< 0.1%
205001
 
< 0.1%
2000010
0.1%

payments
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8681
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1731.830582
Minimum0
Maximum50721.48336
Zeros240
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:21.890586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.86248575
Q1383.280622
median857.8868165
Q31904.981381
95-th percentile6083.430983
Maximum50721.48336
Range50721.48336
Interquartile range (IQR)1521.700759

Descriptive statistics

Standard deviation2884.060385
Coefficient of variation (CV)1.665324782
Kurtosis54.96966457
Mean1731.830582
Median Absolute Deviation (MAD)582.176172
Skewness5.90182428
Sum15447928.79
Variance8317804.306
MonotonicityNot monotonic
2021-11-30T09:51:22.076090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0240
 
2.7%
282.4147091
 
< 0.1%
817.0503771
 
< 0.1%
284.0932611
 
< 0.1%
1333.4466311
 
< 0.1%
1265.310931
 
< 0.1%
453.194281
 
< 0.1%
3530.1317951
 
< 0.1%
414.2180071
 
< 0.1%
2857.8815851
 
< 0.1%
Other values (8671)8671
97.2%
ValueCountFrequency (%)
0240
2.7%
0.0495131
 
< 0.1%
0.0564661
 
< 0.1%
2.3895831
 
< 0.1%
3.5005051
 
< 0.1%
4.5235551
 
< 0.1%
4.8415431
 
< 0.1%
5.0707261
 
< 0.1%
9.0400171
 
< 0.1%
9.5333131
 
< 0.1%
ValueCountFrequency (%)
50721.483361
< 0.1%
46930.598241
< 0.1%
40627.595241
< 0.1%
39461.96581
< 0.1%
39048.597621
< 0.1%
36066.750681
< 0.1%
35843.625931
< 0.1%
34107.074991
< 0.1%
33994.727851
< 0.1%
33486.310441
< 0.1%

minimum_payments
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8607
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean834.2181165
Minimum0
Maximum76406.20752
Zeros313
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:22.255583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.69426285
Q1163.0290433
median289.514532
Q3787.173152
95-th percentile2722.274019
Maximum76406.20752
Range76406.20752
Interquartile range (IQR)624.1441087

Descriptive statistics

Standard deviation2339.137016
Coefficient of variation (CV)2.803987314
Kurtosis291.752683
Mean834.2181165
Median Absolute Deviation (MAD)188.4782655
Skewness13.79779351
Sum7441225.599
Variance5471561.98
MonotonicityNot monotonic
2021-11-30T09:51:22.435956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0313
 
3.5%
299.3518812
 
< 0.1%
1340.3203421
 
< 0.1%
160.8636661
 
< 0.1%
210.1562381
 
< 0.1%
200.1062611
 
< 0.1%
25697.637721
 
< 0.1%
427.147061
 
< 0.1%
227.7265161
 
< 0.1%
438.1905741
 
< 0.1%
Other values (8597)8597
96.4%
ValueCountFrequency (%)
0313
3.5%
0.0191631
 
< 0.1%
0.0377441
 
< 0.1%
0.055881
 
< 0.1%
0.0594811
 
< 0.1%
0.1170361
 
< 0.1%
0.2619841
 
< 0.1%
0.3119531
 
< 0.1%
0.3194751
 
< 0.1%
1.1130271
 
< 0.1%
ValueCountFrequency (%)
76406.207521
< 0.1%
61031.61861
< 0.1%
56370.041171
< 0.1%
50260.759471
< 0.1%
43132.728231
< 0.1%
42629.551171
< 0.1%
38512.124771
< 0.1%
31871.363791
< 0.1%
30528.43241
< 0.1%
29019.802881
< 0.1%

prc_full_payment
Real number (ℝ≥0)

ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.153857934
Minimum0
Maximum1
Zeros5878
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:22.671881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.142857
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.142857

Descriptive statistics

Standard deviation0.2925724792
Coefficient of variation (CV)1.901575509
Kurtosis2.428781777
Mean0.153857934
Median Absolute Deviation (MAD)0
Skewness1.941892824
Sum1372.412771
Variance0.08559865557
MonotonicityNot monotonic
2021-11-30T09:51:22.851416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
05878
65.9%
1487
 
5.5%
0.083333426
 
4.8%
0.166667166
 
1.9%
0.25156
 
1.7%
0.5155
 
1.7%
0.090909153
 
1.7%
0.333333133
 
1.5%
0.194
 
1.1%
0.283
 
0.9%
Other values (37)1189
 
13.3%
ValueCountFrequency (%)
05878
65.9%
0.083333426
 
4.8%
0.090909153
 
1.7%
0.194
 
1.1%
0.11111161
 
0.7%
0.12552
 
0.6%
0.14285754
 
0.6%
0.166667166
 
1.9%
0.18181875
 
0.8%
0.283
 
0.9%
ValueCountFrequency (%)
1487
5.5%
0.91666777
 
0.9%
0.90909119
 
0.2%
0.916
 
0.2%
0.88888912
 
0.1%
0.87518
 
0.2%
0.85714312
 
0.1%
0.83333362
 
0.7%
0.81818217
 
0.2%
0.833
 
0.4%

tenure
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.52073991
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:23.002475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.332919677
Coefficient of variation (CV)0.115697402
Kurtosis7.777202151
Mean11.52073991
Median Absolute Deviation (MAD)0
Skewness-2.955438266
Sum102765
Variance1.776674865
MonotonicityNot monotonic
2021-11-30T09:51:23.123249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
127565
84.8%
11364
 
4.1%
10234
 
2.6%
6200
 
2.2%
8194
 
2.2%
7189
 
2.1%
9174
 
2.0%
ValueCountFrequency (%)
6200
 
2.2%
7189
 
2.1%
8194
 
2.2%
9174
 
2.0%
10234
 
2.6%
11364
 
4.1%
127565
84.8%
ValueCountFrequency (%)
127565
84.8%
11364
 
4.1%
10234
 
2.6%
9174
 
2.0%
8194
 
2.2%
7189
 
2.1%
6200
 
2.2%

purchases_avg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6171
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.92179897
Minimum0
Maximum5981.666667
Zeros2038
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:23.323223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median41.42833333
Q378.810375
95-th percentile228.5795238
Maximum5981.666667
Range5981.666667
Interquartile range (IQR)66.810375

Descriptive statistics

Standard deviation160.6366626
Coefficient of variation (CV)2.17306214
Kurtosis265.0401262
Mean73.92179897
Median Absolute Deviation (MAD)34.08204617
Skewness11.50961785
Sum659382.4468
Variance25804.13738
MonotonicityNot monotonic
2021-11-30T09:51:23.492727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02038
 
22.8%
5030
 
0.3%
45.6527
 
0.3%
6022
 
0.2%
10017
 
0.2%
2516
 
0.2%
4015
 
0.2%
2011
 
0.1%
9011
 
0.1%
4510
 
0.1%
Other values (6161)6723
75.4%
ValueCountFrequency (%)
02038
22.8%
0.014
 
< 0.1%
0.051
 
< 0.1%
0.72
 
< 0.1%
0.73
 
< 0.1%
13
 
< 0.1%
1.481
 
< 0.1%
21
 
< 0.1%
2.7833333331
 
< 0.1%
2.8561
 
< 0.1%
ValueCountFrequency (%)
5981.6666671
< 0.1%
29001
< 0.1%
26001
< 0.1%
2523.4381
< 0.1%
2483.261
< 0.1%
21901
< 0.1%
20002
< 0.1%
18751
< 0.1%
1865.0441
< 0.1%
18101
< 0.1%

cash_advance_avg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4309
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.2463055
Minimum0
Maximum14836.45141
Zeros4612
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2021-11-30T09:51:23.682227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3247.4883825
95-th percentile927.0156037
Maximum14836.45141
Range14836.45141
Interquartile range (IQR)247.4883825

Descriptive statistics

Standard deviation536.4028126
Coefficient of variation (CV)2.563499563
Kurtosis155.1960689
Mean209.2463055
Median Absolute Deviation (MAD)0
Skewness9.618200794
Sum1866477.045
Variance287727.9774
MonotonicityNot monotonic
2021-11-30T09:51:23.849836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04612
51.7%
340.2495391
 
< 0.1%
102.58844561
 
< 0.1%
1603.4048981
 
< 0.1%
753.313511
 
< 0.1%
364.09867161
 
< 0.1%
1851.2378481
 
< 0.1%
908.4340861
 
< 0.1%
181.35161841
 
< 0.1%
338.39419671
 
< 0.1%
Other values (4299)4299
48.2%
ValueCountFrequency (%)
04612
51.7%
14.2222161
 
< 0.1%
18.0427681
 
< 0.1%
18.10147851
 
< 0.1%
18.1179671
 
< 0.1%
18.1234131
 
< 0.1%
18.1263471
 
< 0.1%
18.1266831
 
< 0.1%
18.1499461
 
< 0.1%
18.15430551
 
< 0.1%
ValueCountFrequency (%)
14836.451411
< 0.1%
10590.411131
< 0.1%
9798.1673291
< 0.1%
9671.3367371
< 0.1%
9553.9559061
< 0.1%
7968.2733591
< 0.1%
7894.5788161
< 0.1%
7714.4931221
< 0.1%
7413.653581
< 0.1%
7378.2536851
< 0.1%

Interactions

2021-11-30T09:51:09.048153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:50.003693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:53.867873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:57.683036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:01.856778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:06.112035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:10.110159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:14.375188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:17.957373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:22.050299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:25.718600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:29.439325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:33.439293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:37.180350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:40.935688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:44.970064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:48.889933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:53.155475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:57.305487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:01.013578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:05.146292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:09.233656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:50.269535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:54.030438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:57.852102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:02.043280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:06.288564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:10.303700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:14.535306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:18.113490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:22.208977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:25.880126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:29.604357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:33.640787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:37.347841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:41.106834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:45.139162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:49.056489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:53.342331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:57.469050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:01.183087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:05.319940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:09.419161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:50.472989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:54.195995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:58.040596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:02.241746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:06.484552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:10.499118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:14.693277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:18.271593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:22.372654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:26.045239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:29.768737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:33.810846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:37.517451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:41.287351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:45.316670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:49.226073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:53.529341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:57.632673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:01.351633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:05.495142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:09.619624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:50.711388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:54.380542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:58.292921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:02.444206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:06.890466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:10.707560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:14.874856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:18.665517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:22.554168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:26.235167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:29.952752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:33.995393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:37.707916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:41.556011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:45.511109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:49.416525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:53.731027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:57.812129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:01.538136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:05.684963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:09.804640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:50.931760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:54.549736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:58.506352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:02.644668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:07.070983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:10.903076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:15.045400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:18.831095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:22.727317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:26.409730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:30.125312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:34.167931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:37.881332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:41.751245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:45.726532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:49.592561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:53.923184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:57.985729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:01.718666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:05.868469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:09.988186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:51.110795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:54.723277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:58.691895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:02.830779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:07.241524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:11.102511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:15.210918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:18.993639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:22.896975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:26.581783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:30.296832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:34.340466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:38.052811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:41.933285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:45.921014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:49.765100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:54.110306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:58.153219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:01.899178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:06.046933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:10.190118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:51.300798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:54.917259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:58.900295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:03.069137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:07.431661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:11.311995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:15.399414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:19.181143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:23.086469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:26.773311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:30.488339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:34.535913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:38.244359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:42.132769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-30T09:50:13.376709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:17.084686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:21.144233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:24.814327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:28.534110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:32.233271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:36.282787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:40.013686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:44.002082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:47.933397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:52.207726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:56.308491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:00.093524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:04.146266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:08.105759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:12.686646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:53.181680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:56.984904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:01.043690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:05.301595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:09.340930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:13.590137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:17.268214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:21.334256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:25.009806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:28.731539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:32.431779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:36.473323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:40.215109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:44.201598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:48.135391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:52.438108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:56.520925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:00.290998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:04.410520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:08.304271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:12.871700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:53.349230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:57.154450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:01.227414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:05.493589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:09.518083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:13.782624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:17.433711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:21.503765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:25.180348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:28.904593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:32.605275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:36.642621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:40.392633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:44.380107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:48.317155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:52.611683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:56.716989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:00.467976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:04.586558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:08.482803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:13.059707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:53.516810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:57.324994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:01.423888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:05.698546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:09.697586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:13.971158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:17.599816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:21.676339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:25.354947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:29.078725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:33.045244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:36.820252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:40.568208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:44.569561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:48.503453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:52.783227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:56.907495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:00.645525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:04.771616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:08.665666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:13.250169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:53.686361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:49:57.498528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:01.636347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:05.898567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:09.896547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:14.168635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:17.775347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:21.849836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:25.532000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:29.254889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:33.222736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:36.995783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:40.748680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:44.768596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:48.693498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:52.968729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:50:57.103976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:00.826039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:04.954796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-30T09:51:08.844759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-11-30T09:51:24.162344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-30T09:51:24.721133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-30T09:51:25.178937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-30T09:51:25.636779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-30T09:51:13.635321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-30T09:51:15.884796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcust_idbalancebalance_frequencypurchasesoneoff_purchasesinstallments_purchasescash_advancepurchases_frequencyoneoff_purchases_frequencypurchases_installments_frequencycash_advance_frequencycash_advance_trxpurchases_trxcredit_limitpaymentsminimum_paymentsprc_full_paymenttenurepurchases_avgcash_advance_avg
001000140.9007490.81818295.400.0095.400.0000000.1666670.0000000.0833330.000000021000.0201.802084139.5097870.0000001247.7000000.000000
11100023202.4674160.9090910.000.000.006442.9454830.0000000.0000000.0000000.250000407000.04103.0325971072.3402170.222222120.0000001610.736371
22100032495.1488621.000000773.17773.170.000.0000001.0000001.0000000.0000000.0000000127500.0622.066742627.2847870.0000001264.4308330.000000
33100041666.6705420.6363641499.001499.000.00205.7880170.0833330.0833330.0000000.083333117500.00.0000000.0000000.000000121499.000000205.788017
4410005817.7143351.00000016.0016.000.000.0000000.0833330.0833330.0000000.000000011200.0678.334763244.7912370.0000001216.0000000.000000
55100061809.8287511.0000001333.280.001333.280.0000000.6666670.0000000.5833330.000000081800.01400.0577702407.2460350.00000012166.6600000.000000
6610007627.2608061.0000007091.016402.63688.380.0000001.0000001.0000001.0000000.00000006413500.06354.314328198.0658941.00000012110.7970310.000000
77100081823.6527431.000000436.200.00436.200.0000001.0000000.0000001.0000000.0000000122300.0679.065082532.0339900.0000001236.3500000.000000
88100091014.9264731.000000861.49661.49200.000.0000000.3333330.0833330.2500000.000000057000.0688.278568311.9634090.00000012172.2980000.000000
9910010152.2259750.5454551281.601281.600.000.0000000.1666670.1666670.0000000.0000000311000.01164.770591100.3022620.00000012427.2000000.000000

Last rows

df_indexcust_idbalancebalance_frequencypurchasesoneoff_purchasesinstallments_purchasescash_advancepurchases_frequencyoneoff_purchases_frequencypurchases_installments_frequencycash_advance_frequencycash_advance_trxpurchases_trxcredit_limitpaymentsminimum_paymentsprc_full_paymenttenurepurchases_avgcash_advance_avg
8910894019181130.8385541.000000591.240.00591.240.0000001.0000000.0000000.8333330.000000061000.0475.52326282.7713201.00698.5400000.000000
89118941191825967.4752700.833333214.550.00214.558555.4093260.8333330.0000000.6666670.6666671359000.0966.202912861.9499060.00642.910000658.108410
891289421918340.8297491.000000113.280.00113.280.0000001.0000000.0000000.8333330.000000061000.094.48882886.2831010.25618.8800000.000000
89138943191845.8717120.50000020.9020.900.000.0000000.1666670.1666670.0000000.00000001500.058.64488343.4737170.00620.9000000.000000
8914894419185193.5717220.8333331012.731012.730.000.0000000.3333330.3333330.0000000.000000024000.00.0000000.0000000.006506.3650000.000000
891589451918628.4935171.000000291.120.00291.120.0000001.0000000.0000000.8333330.000000061000.0325.59446248.8863650.50648.5200000.000000
891689461918719.1832151.000000300.000.00300.000.0000001.0000000.0000000.8333330.000000061000.0275.8613220.0000000.00650.0000000.000000
891789471918823.3986730.833333144.400.00144.400.0000000.8333330.0000000.6666670.000000051000.081.27077582.4183690.25628.8800000.000000
891889481918913.4575640.8333330.000.000.0036.5587780.0000000.0000000.0000000.16666720500.052.54995955.7556280.2560.00000018.279389
8919894919190372.7080750.6666671093.251093.250.00127.0400080.6666670.6666670.0000000.3333332231200.063.16540488.2889560.00647.53260963.520004